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Development and evaluation of a daily temporal interpolation model for fine particulate matter species concentrations and source apportionment

机译：精细颗粒物浓度和源分配的每日时间插值模型的开发和评估

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The impacts of emissions sources on air quality in St. Louis, Missouri are assessed for use in acute health effects studies. However, like many locations in the United States, the speciated particulate matter (PM) measurements from regulatory monitoring networks in St. Louis are only available every third day. The power of studies investigating acute health effects of air pollution is reduced when using one-in-three day source impacts compared to daily source impacts. This paper presents a temporal interpolation model to estimate daily speciated PM2.5 mass concentrations and source impact estimates using one-in three day measurements. The model is used to interpolate 1-in-3 day source impact estimates and to interpolate the 1-in-3 day PM species concentrations prior to source apportionment (SA). Both approaches are compared and evaluated using two years (June 2001 May 2003) of daily data from the St. Louis Midwest Supersite (STL-SS). Data withholding is used to simulate a 1-in-3 day data set from the daily data to evaluate interpolated estimates. After evaluation using the STL-SS data, the model is used to estimate daily source impacts at another site approximately seven kilometers (7 km) northwest of the STL-SS (Blair); results between the sites are compared. For interpolated species concentrations, the model performs better for secondary species (sulfate, nitrate, ammonium, and organic carbon) than for primary species (metals and elemental carbon), likely due to the greater spatial autocorrelation of secondary species. Pearson correlation (R) values for sulfate, nitrate, ammonium, elemental carbon, and organic carbon ranged from 0.61 (elemental carbon, EC2) to 0.97 (sulfate). For trace metals, the R values ranged from 0.31 (Ba) to 0.81 (K). The interpolated source impact estimates also indicated a stronger correlation for secondary sources. Correlations of the secondary source impact estimates based on measurement data and interpolation data ranged from 0.68 to 0.97, whereas for primary source contribution estimates the correlations ranged from 0.042 to 0.95. Comparison of daily source impact estimates with source impacts from the interpolation models indicated that interpolation of source contributions was preferable over interpolating species concentrations then applying a SA model. This was based on better agreement in the predicted source impact concentrations and higher correlation with daily SA results. Overall, this study indicates that the temporal interpolation model produces results that may be used to estimate source impacts for health studies, though the additional uncertainty should be considered. (C) 2016 Elsevier Ltd. All rights reserved.
机译：对密苏里州圣路易斯的排放源对空气质量的影响进行了评估，以用于急性健康影响研究。但是，与美国的许多地方一样，圣路易斯监管监视网络中的特定颗粒物（PM）测量仅每三天提供一次。与每日污染源相比，使用三分之一天的污染源影响时，调查空气污染对健康的急性影响的研究能力减弱了。本文提出了一个时间插值模型，以三分之二的一日测量值估算每日指定的PM2.5质量浓度和源影响估算值。该模型用于插值1天3天源影响评估，并插值1天3天PM物种浓度，然后进行源分配（SA）。两种方法都使用来自圣路易斯中西部超级站点（STL-SS）的两年（2001年6月，2001年）每日数据进行比较和评估。数据预扣用于模拟每日数据中的1分3天数据集，以评估插值估算值。在使用STL-SS数据进行评估后，该模型用于估算在STL-SS西北约7公里（7公里）（布莱尔）的另一个站点的每日源头影响；比较站点之间的结果。对于内插物种的浓度，该模型对于次要物种（硫酸盐，硝酸盐，铵和有机碳）的性能要优于主要物种（金属和元素碳），这可能是由于次要物种的空间自相关性更大。硫酸盐，硝酸盐，铵，元素碳和有机碳的皮尔森相关性（R）值介于0.61（元素碳，EC2）至0.97（硫酸盐）之间。对于痕量金属，R值范围为0.31（Ba）至0.81（K）。内插源影响估计值还表明，辅助源的相关性更强。基于测量数据和插值数据的次要来源影响估计的相关性介于0.68至0.97之间，而主要来源贡献的估计值的相关性则介于0.042至0.95之间。将每日源影响估算值与插值模型中的源影响进行比较表明，源贡献插值优于插值物种浓度，然后应用SA模型。这是基于预测的源影响浓度更好的一致性以及与每日SA结果的更高相关性。总体而言，这项研究表明，尽管应考虑其他不确定性，但时间插值模型所产生的结果可用于估算健康研究的源影响。 （C）2016 Elsevier Ltd.保留所有权利。

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